• Who are we?
• Who are you?
• What is expected?
• Why does this class exist?
• Collection
• Changing computing (Parallel / Cloud)
• Course outline

• What is an image?
• Where do images come from?
• Science and Reproducibility
• Workflows

• CTO at 4Quant for Big Image Analytics (ETH Spin-off)
• Lecturer at ETH Zurich

• Postdoc in the X-Ray Microscopy Group at ETH Zurich and Swiss Light Source at Paul Scherrer Institute

• 

• Exercise assistance

• PhD Student in the X-Ray Microscopy Group at ETH Zurich and Swiss Light Source at Paul Scherrer Institute

• 

• Group Leader at the ICON Beamline at the SINQ (Neutron Source) at Paul Scherrer Institute

A wide spectrum of backgrounds

• Biomedical Engineers, Physicists, Chemists, Art History Researchers, Mechanical Engineers, and Computer Scientists

A wide range of skills

• I think I've heard of Matlab before \(\rightarrow\) I write template C++ code and hand optimize it afterwards

Conceptual, graphical assignments with practical examples

• Emphasis on chosing correct steps and understanding workflow

Opportunities to create custom implementations, plugins, and perform more complicated analysis on larger datasets if interested

• Emphasis on performance, customizing analysis, and scalability

• Usually 1 set per lecture
• Optional (but recommended!)
• Easy - using GUIs (KNIME and ImageJ) and completing Matlab Scripts (just lecture 2)
• Advanced - Writing Python, Java, Scala, …

• Optional (but strongly recommended)
• Applying Techniques to answer scientific question!
• Ideally use on a topic relevant for your current project, thesis, or personal activities
• or choose from one of ours (will be online, soon)
• Present approach, analysis, and results

• Jean Claude, Morphometry with R
• Online through ETHZ
• Buy it
• John C. Russ, “The Image Processing Handbook”,(Boca Raton, CRC Press)
• Available online within domain ethz.ch (or proxy.ethz.ch / public VPN)
• J. Weickert, Visualization and Processing of Tensor Fields
• Online

• Imaging
• ImageJ and SciJava
• Cloud Computing
• The Case for Energy-Proportional Computing _ Luiz André Barroso, Urs Hölzle, IEEE Computer, December 2007_
• Concurrency
• Reproducibility
• Trouble at the lab Scientists like to think of science as self-correcting. To an alarming degree, it is not
• Why is reproducible research important? The Real Reason Reproducible Research is Important
• Science Code Manifesto
• Reproducible Research Class @ Johns Hopkins University

- To understand what, why and how from the moment an image is produced until it is finished (published, used in a report, …) - To learn how to go from one analysis on one image to 10, 100, or 1000 images (without working 10, 100, or 1000X harder)

1. Experimental Design finding the right technique, picking the right dyes and samples has stayed relatively consistent, better techniques lead to more demanding scientits.

2. Management storing, backing up, setting up databases, these processes have become easier and more automated as data magnitudes have increased

3. Measurements the actual acquisition speed of the data has increased wildly due to better detectors, parallel measurement, and new higher intensity sources

4. Post Processing this portion has is the most time-consuming and difficult and has seen minimal improvements over the last years

To put more real numbers on these scales rather than 'pseudo-publications', the time to measure a terabyte of data is shown in minutes.

If you looked at one 1000 x 1000 sized image every second, it would take you
139 hours to browse through a terabyte of data.

\[ \textrm{Transistors} \propto 2^{T/(\textrm{18 months})} \]

Based on trends from Wikipedia and Intel

_Based on data from https://gist.github.com/humberto-ortiz/de4b3a621602b78bf90d_

There are now many more transistors inside a single computer but the processing speed hasn't increased. How can this be?

• Multiple Core
• Many machines have multiple cores for each processor which can perform tasks independently
• Multiple CPUs
• More than one chip is commonly present
• New modalities
• GPUs provide many cores which operate at slow speed

Parallel Code is important

• Computer, servers, workstations are wildly underused (majority are <50%)
• Buying a big computer that sits idle most of the time is a waste of money

http://www-inst.eecs.berkeley.edu/~cs61c/sp14/ “The Case for Energy-Proportional Computing,” Luiz André Barroso, Urs Hölzle, IEEE Computer, December 2007

• Traditionally the most important performance criteria was time, how fast can it be done
• With Platform as a service servers can be rented instead of bought
• Speed is still important but using cloud computing $ / Sample is the real metric
• In Switzerland a PhD student if 400x as expensive per hour as an Amazon EC2 Machine
• Many competitors keep prices low and offer flexibility

The figure shows the range of cloud costs (determined by peak usage) compared to a local workstation with utilization shown as the average number of hours the computer is used each week.

The figure shows the cost of a cloud based solution as a percentage of the cost of buying a single machine. The values below 1 show the percentage as a number. The panels distinguish the average time to replacement for the machines in months

A very abstract definition: A pairing between spatial information (position) and some other kind of information (value).

In most cases this is a 2 dimensional position (x,y coordinates) and a numeric value (intensity)

This can then be rearranged from a table form into an array form and displayed as we are used to seeing images

• Impulses Light, X-Rays, Electrons, A sharp point, Magnetic field, Sound wave
• Characteristics Electron Shell Levels, Electron Density, Phonons energy levels, Electronic, Spins, Molecular mobility
• Response Absorption, Reflection, Phase Shift, Scattering, Emission
• Detection Your eye, Light sensitive film, CCD / CMOS, Scintillator, Transducer

• Visible images produced or can be easily made visible
• Optical imaging, microscopy

here the measurement is supposed to be from a typical microscope which blurs, flips and otherwise distorts the image but the original representation is still visible

• Recorded information does not resemble object
• Response must be transformed (usually computationally) to produce an image

here the measurement is supposed to be from a diffraction style experiment where the data is measured in reciprocal space (fourier) and can be reconstructed to the original shape

Copyright 2003-2013 J. Konrad in EC520 lecture, reused with permission

\[ \left[\left([b(x,y)*s_{ab}(x,y)]\otimes h_{fs}(x,y)\right)*h_{op}(x,y)\right]*h_{det}(x,y)+d_{dark}(x,y) \]

\(s_{ab}\) is the only information you are really interested in, so it is important to remove or correct for the other components

For color (non-monochromatic) images the problem becomes even more complicated \[ \int_{0}^{\infty} {\left[\left([b(x,y,\lambda)*s_{ab}(x,y,\lambda)]\otimes h_{fs}(x,y,\lambda)\right)*h_{op}(x,y,\lambda)\right]*h_{det}(x,y,\lambda)}\mathrm{d}\lambda+d_{dark}(x,y) \]

Human eyes have issues

Which center square seems brighter?

Are the intensities constant in the image?

## Reproducibility

Science demands repeatability! and really wants reproducability - Experimental conditions can change rapidly and are difficult to make consistent - Animal and human studies are prohibitively time consuming and expensive to reproduce - Terabyte datasets cannot be easily passed around many different groups - Privacy concerns can also limit sharing and access to data

• Science is already difficult enough
• Image processing makes it even more complicated
• Many image processing tasks are multistep, have many parameters, use a variety of tools, and consume a very long time

How can we keep track of everything for ourselves and others?

• We can make the data analysis easy to repeat by an independent 3rd party

Easy to follow the list, anyone with the right steps can execute and repeat (if not reproduce) the soup

1. Buy {carrots, peas, tomatoes} at market
2. then Buy meat at butcher
3. then Chop carrots into pieces
4. then Chop potatos into pieces
5. then Heat water
6. then Wait until boiling then add chopped vegetables
7. then Wait 5 minutes and add meat

Here it is harder to follow and you need to carefully keep track of what is being performed

Steps 1-4

1. then Mix carrots with potatos \(\rightarrow mix_1\)
2. then add egg to \(mix_1\) and fry for 20 minutes
3. then Tenderize meat for 20 minutes
4. then add tomatoes to meat and cook for 10 minutes \(\rightarrow mix_2\)
5. then Wait until boiling then add \(mix_1\)
6. then Wait 5 minutes and add \(mix_2\)

Clearly a linear set of instructions is ill-suited for even a fairly easy soup, it is then even more difficult when there are dozens of steps and different pathsways

Furthermore a clean workflow allows you to better parallelize the task since it is clear which tasks can be performed independently